Household size and its role in the association between multimorbidity and health and social care outcomes in older adults in Wales: retrospective cohort study
Bibliographic record
Abstract
Objective To examine how the risk of unplanned admission to hospital and transitioning to live in a care home by number of long term conditions varies by household size. Design Retrospective cohort study. Setting Wales Census 2011 household data, linked to the Welsh Secure Anonymised Information Linkage (SAIL) Databank, 27 March 2011 to 26 March 2016. Participants 391 686 residents of Wales recorded in the Wales Census on 27 March 2011, aged ≥65 years, living in Welsh households of one to six residents, registered with a general practitioner contributing data to the SAIL Databank. Main outcome measures Time to the first unplanned hospital admission and time to transition from living at home in the community to living in a care home, for individuals with 0-1, 2-3, or ≥4 long term conditions living alone or in households with two residents or three or more residents. Results Of the 391 686 individuals included, 36.8% lived alone, 54.0% lived in households of two, and 9.2% lived in households with three or more people. The number of long term conditions was strongly associated with the risk of hospital admission and transition to a care home. In those living in two person households, participants with ≥4 long term conditions versus those with 0-1 long term conditions had a higher risk of unplanned hospital admissions (adjusted hazard ratio 2.51, 95% confidence interval (CI) 2.47 to 2.55; crude event rate 180.1 (95% CI 178.5 to 181.7) v 54.8 (53.9 to 55.7) per 1000 person years) and of transitioning to live in a care home (adjusted hazard ratio 2.57, 2.49 to 2.66; crude event rate 7.2 (6.9 to 7.5) v 1.40 (1.3 to 1.5) per 1000 person years). Household size was associated with an increased risk of both outcomes but more strongly with transition to a care home than unplanned hospital admission. The risk of unplanned hospital admissions was higher for people with 0-1 long term conditions who lived alone than for those who lived in a two person household (adjusted hazard ratio 1.19, 95% CI 1.17 to 1.22; crude event rate 74.9 (95% CI 73.4 to 76.4) v 54.8 (53.9 to 55.7) per 1000 person years) and for transitioning to live in a care home (adjusted hazard ratio 1.48, 1.42 to 1.54; crude event rate 5.4 (5.0 to 5.8) v 1.4 (1.3 to 1.5) per 1000 person years). The association between the number of long term conditions and both outcomes varied by household size. Individuals with 0-1 long term conditions and living alone showed a higher risk of transitioning to live in a care home than individuals with 2-3 long term conditions living in two person households. Conclusions In this study, the number of long term conditions was strongly associated with the risk of hospital admission and transition to living in a care home, and this association was less pronounced among those living alone. The risk of transitioning to live in a care home was higher for people with 0-1 long term conditions who lived alone than for those with 2-3 long term conditions who lived in two person households. These findings emphasise the need for personalised strategies that reduce the risk of unplanned admissions to hospital and support independent living, and that consider both the degree of multimorbidity and household size.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".